Development of Probabilistic Forecast Guidance at CIRA Andrea Schumacher (CIRA) Mark DeMaria and John Knaff (NOAA/NESDIS/ORA) Workshop on AWIPS Tools for Probabilistic Forecasting

Outline National Hurricane Center Wind Probabilities –Overview of product –New Application #1: Forecast-Dependent Probabilities –New Application #2: Objective Guidance for U.S. Hurricane Warnings –Improvement (2008): Inland Probability Capping Tropical Cyclone Formation Probability Product –Overview of product / history –Future plans and improvements

NHC Wind Probabilities - The Monte Carlo Wind Probability Model Replaced NHC’s Strike Probability Program in 2006 Included more than track forecast uncertainty –1000 track realizations from random sampling NHC track error distributions Serial correlation and bias of errors accounted for –Intensity of realizations from random sampling NHC intensity error distributions Serial correlation and bias of errors accounted for Special treatment near land –Wind radii of realizations from radii CLIPER model and its radii error distributions Serial correlation included Probability at a point from counting number of realizations passing within the wind radii of interest

1000 Track Realizations 64 kt h Cumulative Probabilities MC Probability Example Hurricane Dean 17 Aug UTC Major Hurricane Non-major Hurricane Tropical Storm Depression

New Application #1: Forecast-Dependent Probabilities Track plots courtesy of J. Vigh, CSU Operational MC model uses basin-wide track error distributions Can situation-dependent track distributions be utilized?

Goerss Predicted Consensus Error (GPCE – “Gypsy”) Predicts error of CONU track forecast –Consensus of GFDI, AVNI, NGPI, UKMI, GFNI GPCE Input –Spread of CONU member track forecasts –Initial latitude –Initial and forecasted intensity Explains 15-50% of CONU track error variance GPCE estimates radius that contains ~70% of CONU verifying positions at each time

Use of GPCE in Wind Probabilities Product database of GPCE values created by NRL Are GPCE radii correlated with NHC and JTWC track errors? –GPCE designed to predict CONU error How can GPCE values be used in the MC model? –MC model uses along/cross track error distributions

Use of GPCE in Wind Probabilities Product Cont… Lower Tercile Distributions Upper Tercile Distributions Hurricane Frances Sept 00 UTC Example 120 hr Cumulative Probabilities for 64 kt

New Application #2: Objective Guidance for US Hurricane Warnings Preliminary rules based on probability analysis* for NHC hurricane warnings –Use 48 h cumulative 64 kt probabilities –Add breakpoint if P ≥ 10 % –Remove breakpoint if P < 1% –Minimum warning length = 40 n mi Except near U.S. boundaries –Update every 6 hours Rules for Watches under development *Mainelli et al 2008, AMS tropical conference

Hurricane Ivan Example 3 NHC Objective Guidance 15 Sep 06 Z (24 h before landfall ) 13 Sep 00 Z

Potential Societal Benefits Objective Guidance Warnings issued a little earlier Warning lowered a little earlier Warnings for Dry Tortugas Gulf coastline length about right What is the impact on warnings from a 20% and 50% reduction in track error? Warning Length Reductions (nmi)

Improvement for 2008: Inland Wind Capping Random perturbations added to inland wind forecasts in 1000 realizations –Max winds of inland storms sometimes unrealistically large –No mechanism for dissipating inland storms –Inland storms can come back to life if sign of random perturbation changes from negative to positive Capping function –Restrict intensity to maximum observed as a function of distance inland –Eliminate rest of track if over-land intensity drops below 15 kt The “no zombie” rule –Capping has no effect on realizations over water

Maximum Observed Sustained Winds for Inland Atlantic Storms ( ) Red curve = empirical “capping” function, Distance to land < 0 for inland storms

0-120 hr Cumulative 34 kt Wind Probabilities (Hurricane Katrina Example) Without Inland Capping With Inland Capping

Tropical Cyclone Formation Probability Product Determines the 24-hour probability of TC formation in each 5 ° x 5 ° latitude/longitude gridbox in its domain Uses environmental predictors (e.g., vertical shear, low-level circulation) and convective/satellite predictors (e.g., percent of pixels colder than -40 ° C)

TCFP Performance Relative maxima agree well with observed TC formations Skill scores indicate TCFP provides skillful forecast (over reference of climatology) However, due to formulation of gridded domain, formation probabilities are generally low (12-20% max) and hence forecasters can be hesitant to use them Example: Hurr. Ike & TS Josephine ~20% improvement in ROC over climatology

The future of the TCFP Improvements on the horizon –Make product global (add S. Hem & Indian Ocean) –Increase length of forecast to 48h + –Add new predictors (e.g., TPW, shear direction) –Develop disturbance-centric scheme Currently, web display only (NESDIS) –Is this the best option? (Recent visit to NHC suggests answer is “not necessarily”)

Summary / Final Thoughts CIRA is active in the development of probabilistic forecast guidance (end users = WFO’s, NHC, JTWC, …) GOES Proving Ground project → more interaction directly with forecasters regarding new satellite guidance products Excited to be here to further relationship between CIRA and ESRL/Global Systems Division Universities Research Institutes Local Forecast Offices National Centers Serve Society’s Needs